Buyse Final

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University of Pennsylvania Annual Conference onStatistical Issues in Clinical Trials

April 13, 2011

Efficient source data verificationin randomized trials

Marc Buyse

IDDI, Louvain-la-Neuve and

I-BioStat, Hasselt University, Belgium



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Outline

1. Trials as a cost-effective, sustainable activity

2. Scientific vs. regulatory requirements

3. The continuum from errors to fraud

4. Monitoring strategies

± Extensive monitoring

± Reduced monitoring

± Targeted monitoring5. The SMART project

6. Conclusions



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Potential reductions in clinical trial costs

Assumptions: Treatment of chronic disease

20,000 patients

1,000 sites

48 months enrollment (24) + follow-up (24)

24 visits per site (every other month)

60-page CRF

10,000 $ per patient site

Total budget in millions of $: 421 $ Coordinating Center 170 $ (40%)

Site payments 200 $ (48%)

Other costs: travel, meetings, etc 51 $ (12%)

Ref: Ei senst ein et al, Clinical Trial s 2008;5:75 .



4Ref: Ei senst ein et al, Clinical Trial s 2008;5:75 .

Potential reductions in clinical trial costs 4 mths planning

24 mths accrual

1,000 sites

24 site visits

60-page CRF

10,000 $ per site





4 mths planning

18 mths accrual 750 sites

4 site visits

20-page CRF + EDC

5,000 $ per site

4 mths planning

24 mths accrual

1,000 sites

24 site visits

60-page CRF

10,000 $ per site





4 mths planning

18 mths accrual

100 sites

no site visits

5-page CRF + EDC

650 $ per site

4 mths planning

18 mths accrual 750 sites

4 site visits

20-page CRF + EDC

5,000 $ per site

4 mths planning

24 mths accrual

1,000 sites

24 site visits

60-page CRF

10,000 $ per site



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Scientific vs. regulatory requirementsfor a clinical trial

From a scient ific point of view, a trial must estimate the effectof a treatment without bias.

Randomized trials enable such unbiased inference even in

the presence of massive random errors which only causeconservatism (in tests for superiority).

From a regulatory point of view, a trial must provide verifiableevidence that it was carried out according tospecifications.

Absence of errors must be demonstrated regardless of their consequences.



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The continuum from errors to fraud

Type Typical examples Intent

Errors Poorly calibratedequipment

Wholly unintentional

Sloppiness Data missing orincorrectly copied fromsource documents

Limited awareness

Fraud Data fabricated to

avoid missing data orcreate patients

Deliberate

Treatment -relatedfraud

Data fabricated orfalsified to favortreatment

Definite « intentionto cheat »



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Type Typical examples Impact


Potential (small) lossin power / no bias


Potential (small) lossin power / no bias



Unknown effect on

power / no bias



Definite bias




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Type Typical examples Ease of detection


Difficult to detect


May be hard todetect



Detectable through

center comparisons



Detectable throughtreatment by centercomparisons




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Extensive monitoring 100% SDV for primary and key secondary

outcomes

Reduced monitoring Random sampling of centers / patients /

outcomes to ensure rate of errors < x%

Risk-adapted monitoring

Targeted monitoring

Monitoring based on Key Risk Indicators

Statistical Monitoring

Monitoring strategies



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outcomes




Targeted monitoring






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Extensive monitoring

´(...) trial management procedures ensuring validity andreliability of the results are vastly more important thanabsence of clerical errors.

Yet, it is clerical inconsistencies referred to as ¶errors¶ thatare chased by the growing GCP-departments.´

Ref s: Lör st ad, ISCB-27, Genev a, August 28-31, 2006



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« Monitoring confirms consistency between datacollection forms and source documents; if the sourcedocuments are wrong because of laboratory, clinical,or clerical errors, then monitoring adds expensewithout benefit. A common misinterpretation of sponsors is that GCP requires audits of 100% of data;by contrast, random audits might suffice. »



17Ref: Glickman et al, NEJM 2009;360:816 .



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outcomes




Targeted monitoring






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Reduced monitoring

Random sampling

CountriesCountriesCountriesCountries Countries

CountriesCountries

Centers

CountriesCountriesCountries

PatientsCountriesCountriesCountries

VisitsCountriesCountriesCountries

Items



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Risk A ± Negligible risk (non invasive procedures)

Risk B ± Risk similar to that of usual care (trials involving

approved drugs)

Risk C ± High risk (phase III trials of new agents, newindications or at risk populations)

Risk D ± Very high risk (phase I or II trials of new agents)



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OPTIMON: OPTimisation of MONitoringfor clinical research studies

Control(³pharma´ st andard s)

Experimental( less v i si ts / c heck s)

Centers

accruing

> 5 patients

in severaltrials

Trialsstratified byrisk group:

- A

- B- C

S ource: Genev ièv e C hêne, Uni v er si t y Teac hing H os pi t al Bordeaux, France

https://ssl2.isped.u-bordeaux2.fr/optimon/Documents.aspx

Goal: non-inferiority of the proportion of patients with at least one severeerror in informed consent, suspected unexpected serious adverse eventsreports, major eligibility criteria, or primary endpoint (expected: 95% withnon-inferiority margin of 5%).



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outcomes




Targeted monitoring






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Targeted monitoringbased on Key Risk Indicators


CountriesCountries

Centers




Items

MonitoringTeam

DataManagement



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Examples of ³Key Risk Indicators´

Study conduct

Actual accrual vs. target

% pts with protocol violations

% dropouts

«



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Study conduct



% dropouts

«

Treatment compliance

% dose reductions

% dose delays

Reasons for Rx stops

«



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Study conduct



% dropouts

«


% dose reductions

% dose delays


«

Safety

AE rate

AE grade 3/4 rate

SAE rate

«



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Study conduct



% dropouts

«


% dose reductions

% dose delays


«

Safety

AE rate

AE grade 3/4 rate

SAE rate

«

Data management

Overdue forms

Query rate

Query resolution time

«



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Targeted monitoring ± based onstatistical monitoring


CountriesCountries

Centers




Items

MonitoringTeam

SMART

DataManagement



29Ref: Baigent et al, Clinical Trial s 2008;5:49.

(«)

Targeted monitoring



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Principles behind statistical checks

Plausible data are hard to fabricatep c heck plausibili t y ( e.g . mean, v ariance, c orrelat i on st ruc t ure, out lier s,inlier s, dat es, et c .)

Humans are poor random number generatorsp c heck rand omness ( e.g . Benf ord¶ s law f or fir st digi t , digi t preference, et c .)

Clinical trial data are highly structuredp check c om parabili t y ( e.g . bet ween cent er s, t reat ment arms, et c .)



31Ref: Encyclopaedic Companion to Medical Statistics (Everitt B, Palmer C, Eds..) Arnold Publishers Ltd, London, 2010.



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SMART*

A software that systematically performs alarge battery of statistical tests on the values

of all variables collected in a clinical trial.These tests generate a large number of p-values, ranks and other statistics that are keptin a database for checks of randomness,

plausibility and comparability.

* Statistical Monitoring Applied to Randomized Trials



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Brute force approach

In multicentric trials, the distribution of allvariables can be compared between eachcenter and all other centers

These tests can be applied automatically,without regard to meaning or plausibility

They yield very large number of center-specific statistics

Meta-statistics can be applied to thesestatistics to identify outlying centers



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An example

Trial in depression

Two stages:

± an open-label run-in treatment stage

± a double-blind randomized treatment stage

} 800 patients from } 70 centers



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Exemplary findings:heart rate/blood pressure

To be taken at each visit, in two positions (supine/standing)

Variability suspiciously low for several centers

³Strange´ patient:

VISIT POS HR SYSBP DIABP

1 1 72 115 75

1 2 70 110 70

2 1 72 115 75

2 2 70 110 70

3 1 70 110 75

3 2 70 110 70

4 1 72 110 75

4 2 70 105 70

5 1 74 115 75

5 2 72 110 70

... ... ... ... ...



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Exemplary findings:heart rate/blood pressure

To be taken at each visit, in two positions (supine/standing)

Variability suspiciously low for several centers

³Strange´ patient:

VISIT POS HR SYSBP DIABP

1 1 72 115 75

1 2 70 110 70

2 1 72 115 75

2 2 70 110 70

3 1 70 110 75

3 2 70 110 70

4 1 72 110 75

4 2 70 105 70

5 1 74 115 75

5 2 72 110 70

... ... ... ... ...

I s i t w or t h asking f or inessent ial,

t edi ous measurements?



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Exemplary findings:baseline MADRS score

MADRS score (the sum of results on 10 questions) < 12needed to enter the randomized stage

Half of the patients were expected to have a score < 12after the run-in period



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In reality, 67% had a score < 12



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³Strange´ centers :

± Center A: 5 8 5 4 7 8 9 4 6 5 7 5 4 3

± Center B: 11 11 11 11 10 11 11 11 11



In reality, 67% had a score < 12



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Conclusions

Current clinical research practices (such asintensive monitoring and 100% source dataverification) are not useful, effective, or sustainable

A statistical approach to quality assurancecould yield huge cost savings and yetincrease the reliability of the trial results

Regulatory requirements should evolveaccordingly

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